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A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles

September 2018
Environmental Science and Technology 52(20):11449-11465

September 2018
52(20):11449-11465

DOI:10.1021/acs.est.8b00127

Authors:

Morteza Taiebat

University of Michigan

Hannah Safford

Federation of American Scientists

Shen Qu

Beijing Institute of Technology

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Connected and automated vehicles (CAVs) are poised to reshape transportation and mobility by replacing humans as the driver and service provider. While the primary stated motivation for vehicle automation is to improve safety and convenience of road mobility, this transformation also provides a valuable opportunity to improve vehicle energy efficiency and reduce emissions in the transportation sector. Progress in vehicle efficiency and functionality, however, does not necessarily translate to net positive environmental outcomes. Here we examine the interactions between CAV technology and the environment at four levels of increasing complexity: vehicle, transportation system, urban system, and society. We find that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV technology directly. We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system levels, but expect greater vehicle utilization and shifts in travel patterns at the society level to offset some of these benefits. Focusing on the vehicle-level improvements associated with CAV technology is likely to yield excessively optimistic estimates of environmental benefits. Future research and policy efforts should strive to clarify the extent and possible synergetic effects from a systems level in order to envisage and address concerns regarding the short-and long-term sustainable adoption of CAV technology.

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A Review on Energy, Environmental, and Sustainability Implications

of Connected and Automated Vehicles

Morteza Taiebat,

†,‡

Austin L. Brown,

Hannah R. Saﬀord,

∇

Shen Qu,

†

and Ming Xu*

,†,‡

†

School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan 48109, United States

‡

Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States

Policy Institute for Energy, Environment, and the Economy, University of California, Davis, California 95616, United States

∇

Department of Civil & Environmental Engineering, University of California, Davis, California 95616, United States

SSupporting Information

ABSTRACT: Connected and automated vehicles (CAVs)

are poised to reshape transportation and mobility by replacing

humans as the driver and service provider. While the primary

stated motivation for vehicle automation is to improve safety

and convenience of road mobility, this transformation also

provides a valuable opportunity to improve vehicle energy

eﬃciency and reduce emissions in the transportation sector.

Progress in vehicle eﬃciency and functionality, however, does

not necessarily translate to net positive environmental

outcomes. Here, we examine the interactions between CAV

technology and the environment at four levels of increasing

complexity: vehicle,transportation system,urban system, and

society.Weﬁnd that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV

technology directly. We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system

levels, but expect greater vehicle utilization and shifts in travel patterns at the society level to oﬀset some of these beneﬁts.

Focusing on the vehicle-level improvements associated with CAV technology is likely to yield excessively optimistic estimates of

environmental beneﬁts. Future research and policy eﬀorts should strive to clarify the extent and possible synergetic eﬀects from

a systems level to envisage and address concerns regarding the short- and long-term sustainable adoption of CAV technology.

1. INTRODUCTION

The fuel-based transportation system holds considerable

inﬂuence over human interactions with the environment.

Transportation directly generated over 7 gigatons of carbon

dioxide equivalent (GtCO2equiv) greenhouse gas (GHG)

emissions worldwide in 2010 or 23% of total global energy-

related GHG emissions.

Annual transportation GHG

emissions are increasing at a faster rate than emissions from

any other sector (i.e., power, industry, agriculture, residential,

or commercial). With income rising and infrastructure

expanding around the world, transportation demand is

expected to increase dramatically in the coming years. Annual

transportation sector emissions are expected to double by

2050.

In the U.S., the transportation sector was the largest source

of GHG emissions in 2016, accounting for 28.5% of total

national energy-related GHG emissions, according to the U.S.

Environmental Protection Agency (EPA).

The most recent

data from the U.S. Energy Information Administration (EIA)

also shows that carbon dioxide (CO2) emissions from the U.S.

transportation sector (1893 million metric tons or MMt)

surpassed CO2emissions from the electric power sector (1803

MMt) from October 2015 through September 2016.

This is

the ﬁrst time that transportation-sector CO2emissions have

regularly exceeded CO2emissions from the electric power

sector since the late 1970s on a 12-month rolling basis. This

trend is likely to continue if growth in renewable energy lowers

fossil fuel-based electricity generation, leading to continued

reduction of power sector emissions.

Within the transportation sector, road-based travel is

responsible for the largest share of CO2emissions, GHG

emissions, and energy use compared to other modes of

transportation such as aviation, rail, and marine. Passenger

cars, light-duty trucks (including sport utility vehicles, pickup

trucks, and minivans), and freight trucks emitted 41.6%, 18.0%,

and 22.9%, respectively, of total U.S. transportation-sector

GHG emissions in 2016.

Given that emissions from the

transportation sector increased more in absolute terms than

emissions from any other sector from 1990−2016, trans-

portation emissions must be a key focus of mitigation eﬀorts.

Strategic development and deployment of new technologies to

Received: January 8, 2018

Revised: August 24, 2018

Accepted: September 7, 2018

Published: September 7, 2018

Critical Review

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curb the environmental impacts of road-based travel can

therefore go a long way toward alleviating the environmental

impacts of the transportation sector overall. One example with

considerable potential to reduce emissions from road-based

travel is connected and automated vehicle (CAV) technology.

Vehicle connectivity and automation are separate technol-

ogies that could exist independent of each other, but entail

strong complementary attributes. Connectivity refers to a

vehicle’s capacity to exchange information with other vehicles

and infrastructure. This capacity can be realized through

vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and

other cooperative communications networks. Vehicle con-

nectivity is a key enabler of vehicle automation. Vehicle

automation refers to any instance in which control of a vehicle

capability normally overseen by a human driver is ceded to a

computer. Examples of automation commonly seen in vehicles

on the market today include cruise control, adaptive cruise

control, active lane-keep assist, and automatic emergency

braking. A fully automated vehicle can navigate itself by

sensing and interacting with the driving environment to reach

its destination without human intervention.

4−6

It is worth noting that the terms “autonomous”and

“automated”are often used interchangeably in the literature,

but merit distinction. The former (a subset of the latter) refers

to a vehicle capable of navigating without direct input from a

human driver and self-driving is possible with limited or no

communication with other vehicles or infrastructure, while the

latter indicates broader classes of vehicle automation. In this

article, the term “CAV technology”refers to vehicle technology

with high levels of automation, as well as connectivity

capabilities. These two facets of CAV technology are expected

to develop in concert.

The Society of Automotive Engineers (SAE) International’s

J3016 taxonomy classiﬁes vehicle automation by level of driver

intervention and/or attentiveness required for operation.

avoid redundancy and confusion, the U.S. National Highway

Traﬃc Safety Administration (NHTSA) agreed to adopt the

SAE’s categorization, instead of relying on vehicle capabilities.

In 2016, the NHTSA proposed mandating V2V connectivity

capability on all new cars and light-duty trucks, citing

signiﬁcant potential safety beneﬁts.

On September 12, 2017,

the U.S. Department of Transportation released updated

federal guidelines for the deployment of highly automated

vehicle technologies.

These guidelines focus on road safety

performance and mobility services, without addressing

environmental impacts.

The primary purpose of CAV technology is to increase

transportation safety and provide better mobility services.

However, vehicle connectivity and automation will also

inevitably and signiﬁcantly change the environmental proﬁle

of the transportation sector.

11−15

A growing body of literature

has examined the possible environmental implications of

CAVs, and has found large uncertainty based in part on the

shortage of real-world data for CAV operations.

CAV

technology could facilitate either dramatic decarbonization of

transportation or equally dramatic increases in transportation-

sector emissions. The net environmental impacts of CAV

technology depend on lawmaking and decisions at the

international, federal, state, and local levels. With the transition

to automated road transportation still in its infancy, there is an

opportunity to work proactively to ensure that CAV

technology develops sustainably. A forward-looking perspec-

tive is needed to properly design, plan, and develop a CAV

system that provides both better mobility service and better

environmental outcomes.

This article is intended to foster understanding and

discussion of the likely and potential environmental

implications of CAV technologies by reviewing existing studies

and identifying key research needs. We deﬁne environmental

impacts broadly in this paper, including not only downstream

emissions and wastes, but also upstream resource and energy

demands. We also discuss some socioeconomic aspects of CAV

adoption that are associated with energy and the environment.

Our review includes some environmental impacts that could be

realized through vehicle automation alone, but most impacts

require automation in conjunction with connectivity. For

simplicity, we attribute all impacts to CAV technology. The

article is organized as follows. We begin by developing a

holistic framework for analyzing diﬀerent levels of interactions

between CAVs and the environment (section 2). We then

survey the quantitative results of relevant studies and critically

evaluate the key assumptions and conclusions of each (section

3). Finally, we identify knowledge gaps and oﬀer recom-

mendations for future research (section 4).

2. LEVELS OF INTERACTIONS BETWEEN CAVS AND

THE ENVIRONMENT

CAV technology interacts with the environment at diﬀerent

scales and levels of complexity. We deﬁne four levels of

interactions between CAVs and the environmentthe vehicle

level, transportation system level, urban system level, and society

levelas illustrated in Figure 1. Interactions generally increase

in complexity from the vehicle level to society level and may

stem from CAV technology directly or CAV-facilitated eﬀects.

The most direct and well-studied interactions occur at the

vehicle level. At this level, connectivity and automation

Figure 1. Levels of interactions between CAVs and the environment

and corresponding major inﬂuence mechanisms.

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11450

Table 1. Summary of Key Environmental Impacts at Each Level of CAV-Environment Interaction

Major Inﬂuencing Mechanisms Positive Impacts Negative Impacts Sources

Vehicle •vehicle operation •higher energy eﬃciency •faster highway speeds 4,12−14,16−22

•vehicle design •optimal driving cycle •additional ICT equipment needs for navigation and communication

•electriﬁcation •eco-routing •aerodynamic shape alteration

•platooning •reduce cold starts •higher auxiliary power requirement

•less idling

•less speed ﬂuctuations

•powertrain downsizing

•self-parking

•safety-enabled vehicle light-weighting

•vehicle right-sizing

•complementary electriﬁcation beneﬁts

•platooning

Transportation

System

•travel-cost implications •greatly reduced human labor costs •higher vehicle utilization rate 12−14,16,17,19,

23−32

•changed mobility services •promotion of shared mobility •more frequent and longer trips result in greater VMT

•vehicle utilization •integration with mass transit •more unoccupied travel (for parking, between trips, etc.)

•congestion and road capacity •ﬂeet downsizing •congestion increases due to induced travel

•increased eﬀective roadway capacity •competition with mass transit

•decongestion

•fewer crashes and less accident-related traﬃc

•syncing with traﬃc lights

Urban System •infrastructure implications •changes in land-use patterns •increased urban sprawl 14,24,33−36

•integration of CAVs with power systems •reduced need for parking infrastructure •need for large, energy-intensive data centers

•land use •integration with power systems through vehicle

electriﬁcation

•reduced need for highway lighting and traﬃc signals

Society •behavior response and travel pattern shift •promotion of shared consumption •induced travel demand and rebound eﬀect 16,17,25,37−42

•shared consumption •spillover eﬀects to other sectors •transportation modal shift (e.g., rail/aviation to road travel)

•transformation of other sectors •gradual unemployment and job displacement

•workforce impacts

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11451

physically alter vehicle design and operation. At the trans-

portation system level, CAV technology can drastically change

how vehicles interact with each other in the driving

environment. At the urban system level, CAV-based trans-

portation interacts with a wide range of infrastructure in the

urban environment such as roads, power grid, and buildings,

thereby altering how urban systems utilize resources and

energy and generate emissions and waste. Finally, how the

public perceives and how the government regulates CAVs can

have profound eﬀects at the society level.

Generally, higher-level interactions will have farther-reaching

implications despite often receiving less attention (Table 1).

Higher-level interactions are also more diﬃcult to quantify and

are associated with greater uncertainty. Many important

questions at high levels are beyond the scope of quantitative

or predictive modeling and must instead be addressed

qualitatively. Because research focusing on CAV environmental

implications is just emerging in recent years, a large body of

literature is in the form of reports and white papers. In order to

make this review as comprehensive as possible, our analysis is

based on not only peer-reviewed studies but also reputable

reports and documents containing consensus quantitative

results. Key sources are classiﬁed based on scope in Table 2.

3. ENVIRONMENTAL IMPACTS OF CAV AT EACH

SYSTEM LEVEL

3.1. Vehicle Level. At this level, we consider the direct

environmental eﬀects of CAV technology on a per vehicle

basis. These eﬀects can also manifest in ﬂeets. Many studies

have focused on the vehicle level and show that individual

CAVs are generally more energy eﬃcient and generate less

emissions than conventional vehicles.

12,13,16

These beneﬁts at

the vehicle level can be attributed to four major factors:

operation, electriﬁcation, design, and platooning.

3.1.1. Vehicle Operation. A number of references discuss

the potential for vehicle automation to improve car-centric

energy eﬃciency by optimizing vehicle operation: that is, by

maximizing the operation of vehicles at the most eﬃcient

mode.

6,14,19,30

Eﬃcient driving broadly translates into

improved fuel economy, reduced energy consumption, and

abated tailpipe emissions. Higher driving eﬃciency can be

achieved in CAVs through a variety of mechanisms, including

optimal driving cycle, dynamic eco-routing, less idling,

reducing cold starts, trip smoothing, and speed harmoniza-

tion.

12,14,28,29,59

These mechanisms are discussed below.

Diﬀerent human drivers in identical situations make diﬀerent

real-time decisions, often leading to suboptimal results.

CAVs, eliminating heterogeneity between drivers and improv-

ing driving decision-making helps optimize the driving cycle.

Barth and Boriboonsomsin reported that, even when drivers

remain “in the loop”of vehicle operation (i.e., at a level of

involvement less than conventional driving but one that falls

short of full automation), providing dynamic feedback to

drivers results in up to 20% fuel savings and decreased CO2

emissions without a signiﬁcant increase in travel time.

The

information gathered from vehicle connectivity also enables

optimal route selecting, widely known as dynamic eco-

routing.

19,30,63

Gonder et al. estimated the potential energy

savings of eco-routing in a Chevy Bolt at around 5%.

Trip

smoothing and speed harmonization are other practices that

aim to minimize repeated braking-acceleration cycles through

intelligent speed adaption, smooth starts, fewer speed

ﬂuctuations, and eliminating unnecessary full stops.

CAV technology substantially facilitates and ampliﬁes these

practices. Wu et al. estimated that partial automation in

conjunction with connectivity can reduce fuel use by 5−7%

compared to human driving when automation enables vehicles

to closely follow recommended speed proﬁles.

At the ﬂeet

level, cooperative communications between vehicles can

further reduce energy use, with up to 13% fuel savings and

12% reductions in CO2emissions reported in experiments.

Prakash et al. suggested that 12−17% reduction in fuel use can

be achieved when a CAV is trailing a lead vehicle with the

speciﬁc objective of minimizing accelerations and deceler-

ations.

On the basis of experiments, Stern et al. found that

introducing even a single CAV into traﬃc dampens stop-and-

go patterns, results in up to 40% reductions in total traﬃc fuel

Table 2. Classiﬁcation of relevant CAV studies by scope

Study

Vehicle Transp.

sys. Urban

sys. Society

Alonso-Mora et al.

√

Anderson et al.

√√ √√

Auld et al.

√

Bansal and Kockelman

√

Barth et al.

√√

Bauer et al.

√√

Brown et al.

√√ √√

Chen et al.

√√

Chen et al.

√

Childress et al.

√√√

Crayton and Meier

√

Fagnant and Kockelman

√√

Fox-Penner et al.

√

Fulton et al.

√√

Gawron et al.

√

Gonder et al.

√

Greenblat and Shaheen

√√ √√

Greenblatt and Saxena

√√ √

Harper et al.

√

Heard et al.

√

Kang et al.

√√ √

Kolosz and Grant-Muller

√√

König and Neumayr

√

Kyriakidis et al.

√

Lavrenz and Gkritza

√√

Li et al.

√√

Liu et al.

√

Lu et al.

√√

Malikopoulos et al.

√√

Mersky and Samaras

√

Moorthy et al.

√

Prakash et al.

√

Rios-Torres and Malikopoulos

√√

Stephens et al.

√√ √√

Stern et al.

√

Wadud

√√

Wadud et al.

√√ √√

Wang et al.

√

Wu et al.

√

Zakharenko

√√

Zhang et al.

√

Zhang et al.

√

Sorted alphabetically based on ﬁrst author.

Publication in a peer-

reviewed journal.

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consumption.

Rios-Torres and Malikopoulos developed a

simulation framework for mixed traﬃc (CAVs interacting with

human-driven vehicles) and reported that the fuel-consump-

tion beneﬁts of CAVs increase with higher CAV penetration.

Chen at al. suggested a wider range of changes in fuel

consumption (between −45% to +30%) that would result from

transitioning from conventional to CAV ﬂeets at the U.S.

national level.

Less idling and fewer cold starts can help reduce energy

waste and mitigate emissions. Cold starts are a major

contributor to a number of criteria air pollutants from the

transportation sector, including volatile organic compounds

(VOCs), NOx, and CO.

Simulations demonstrated fewer

cold starts for shared automated taxis.

In such vehicles, since

no aggressive acceleration is needed, powertrains can also be

downsized. This is especially relevant for automated shared

mobility services in urban areas where more energy use is due

to acceleration rather than from high-speed wind resistance.

Self-parking features also save time and limit braking-

acceleration cycles, reducing energy intensity by approximately

4%.

On the other hand, some attributes of CAVs may result in

more energy consumption. Radar, sensors, network commu-

nications, and high-speed Internet connectivity require higher

auxiliary power from vehicles, which manifests as greater power

draw and consequently higher energy consumption.

Energy

demands for connectivity components, sensing, and computing

equipment can signiﬁcantly alter the overall energy eﬃciency

of CAVs.

Additionally, improved safety in CAVs may induce

higher highway speeds. Since aerodynamic drag forces increase

quadratically with speed, higher highway speeds result in

higher fuel consumption above a certain threshold.

For

instance, a speed increase from 70 to 80 mile per hour (MPH)

is reported to increase average energy use by 13.9% per mile.

Wadud et al. and Brown et al. suggested that typical driving at

above-optimal speeds tends to decrease overall fuel economy

by 5−22%.

12,14

This decrease may oﬀsetand indeed,

overwhelmincreases in engine eﬃciency. It is conceivable

that improved safety in CAVs could enable relaxation of speed

limits for roadways where vehicles are currently restricted to

below-optimal speeds, resulting in some energy savings. This

point received less attention in the literature.

The extent to which CAV-related increases in vehicle energy

consumption will oﬀset gains in energy eﬃciency is unclear.

CAV technology could lead to substantial net improvements in

fuel economy and emissions reduction if the negative eﬀects

are minimized and the positive realized. Mersky and Samaras

raised the question of how to test and measure fuel eﬃciency

of CAVs by updating EPA rating tests.

They developed a

method for testing fuel economy of CAVs using the existing

EPA test procedure and showed that fuel economy diﬀerences

for the CAV tests range from −3% to +5% compared to the

current EPA testing procedure.

3.1.2. Electriﬁcation. Many studies examining the environ-

mental externalities of vehicle electriﬁcation have found that

electric vehicles (EVs) usually improve environmental out-

comes and remove local pollution from urban cores.

66,67

The

speciﬁc environmental impacts of EVs are largely determined

by when cars are charged and where and how chargers are

integrated into the electric grid. Emissions from power

generation for EVs might in some cases be higher than

tailpipes emissions from vehicles with internal combustion

engines. However, moving emissions from a large number of

individual vehicle tailpipes to a few centralized power plants is

likely to reduce emission mitigation costs, improve energy

eﬃciency, and help integrate renewable energy in power

generation.

Oﬀer et al. demonstrated that plug-in hybrid

electric vehicles (PHEVs) and battery electric vehicles (BEVs)

have much lower life-cycle costs and emissions compared to

fuel cells or internal combustion engines vehicles.

Despite

potential beneﬁts, the actual environmental impacts of EVs are

aﬀected by many factors, such as unregulated charging, vehicle-

to-grid (V2G) communications, charge speed, and the degree

to which users overcome range anxiety. The eﬀects of these

factors remain uncertain and require more research.

CAV technology can provide a strong complement to EV

technology, potentially solving some of the challenges of EV

development.

In electric CAVs, on-board energy manage-

ment strategies can be explicitly designed and implemented to

take advantage of synergies between electriﬁcation and

automation. For instance, an electric CAV could optimize

route selection and driving cycle to reduce battery draining,

maximize energy recovery via regenerative braking, and extend

the battery life.

CAVs can also mitigate the range restriction of EVs by

matching appropriately ranged vehicles to individual trips,

and take advantage of the energy and environmental beneﬁts

brought by vehicle electriﬁcation. Oﬀer argued that even if

electric CAVs substantially increase vehicle utilization, they will

have a large positive impact on transport decarbonization and

will curb global GHG emissions by improving the economics

of electriﬁcation.

Shared automated electric vehicles

(SAEVs) magnify beneﬁts by orders of magnitude.

Green-

blatt and Saxena suggested that electric automated taxis can

reduce per-mile GHG emissions by more than 90% compared

to using conventional vehicles for daily travel.

Bauer et al.

simulated the operation of SAEVs in NYC, and found that

under the current power-grid mix, SAEV ﬂeet would generate

73% fewer GHG emissions and consume 58% less energy than

a nonelectriﬁed automated ﬂeet.

3.1.3. Vehicle Design. The size and weight of a vehicle have

direct impacts on the vehicle’s fuel economy, and consequently

on its overall environmental performance. The composition of

the vehicle body indirectly inﬂuences the life-cycle environ-

mental impacts of the vehicle via resource and energy

requirements associated with the supply chain. CAV engineer-

ing is expected to enable a number of eﬃciency-improving

design practices, such as vehicle right-sizing and safety-enabled

vehicle light-weighting. On the other hand, more carbon-

intensive materials are needed in CAVs, which could increase

overall per-vehicle weight as well. Diﬀerences in CAV design

strategies among automakers and the evolution of Evolution of

design design over time add uncertainties to analysis of CAV-

related environmental impacts.

3.1.3.a. Vehicle Light-Weighting. A number of recent

studies have addressed the life-cycle environmental impacts of

vehicle light-weighting using alternative materials. Several

report that each 10% reduction in vehicle weight yields on

average a direct fuel economy improvement of 6−8%.

14,69

In a highly connected and automated vehicle system,

transportation safety can be signiﬁcantly improved by

eliminating human errors in driving. As a result, once CAVs

make up the vast majority of on-road active vehicles,

crashworthiness of vehicles becomes less crucial, and vehicles

can become smaller with less safety equipment. Safety features

contributed to 7.7% of total vehicle weight in an average new

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U.S. vehicle in 2011.

If these features could be safely

removed, an estimated 4.6−6.2% improvement in fuel

economy could be realized.

Moreover, environmental

impacts associated with the life-cycle of the eliminated vehicle

safety features could also be avoided.

Reduced safety equipment in CAVs also leads to more

optimal and smaller powertrains, further improving fuel

economy. Wadud et al. suggested “de-emphasized perform-

ance”as another potential option that would further downsize

the powertrain of CAVs and save up to 5% of fuel

consumption.

Conventional vehicles typically have power

capabilities far in excess of their average power requirements to

satisfy occasional high-performance demands, such as freeway

merging. The ability of CAVs to smooth speed proﬁles,

coupled with the high potential of CAVs to serve in shared

mobility services, means that peak power demand could be

signiﬁcantly reduced.

3.1.3.b. Vehicle Right-Sizing. Another opportunity that

could be realized from widespread use of CAVs is vehicle

“right-sizing”. According to 2017 National Household Travel

Survey, single- and double-occupant vehicle trips respectively

accounted for 58% and 25% of total annual vehicle-miles-

traveled (VMT) in passenger trips made in the U.S., and the

average occupancy of light-duty vehicles was just 1.67

passengers.

There is signiﬁcant potential for vehicle size

optimization by matching speciﬁc vehicles to speciﬁc trips to

avoid wasted capacity and thus associated environmental

impacts. In the case of automated taxis or shared automated

vehicles (SAVs), a vehicle could be dispatched based on a

passenger’s needs (e.g., a smaller vehicle for a solo traveler).

Greenblatt and Saxena studied trip-speciﬁc (i.e., right-sized)

automated taxis based on the average proportion of occupants

and total VMT. They concluded that trip-speciﬁc automated

taxis could improve the fuel eﬃciency of ﬂeets by 30−35%.

Wadud et al. investigated an extreme scenario in which all trips

occur in optimally sized vehicles. In this scenario, solo travelers

travel in single-occupant CAVs with the energy eﬃciency of

motorcycles (half the fuel economy of a compact car), two-

person groups travel in compact cars, groups of 3−4 travel in

midsize vehicles, and groups of 5 or more travel in minivans.

They reported that such a scenario would yield fuel savings of

45%.

While right-sizing 100% of vehicle trips may be an

unrealistic goal, this demonstrates the high potential of CAV

right-sizing for improving fuel economy and consequently

reducing environmental impacts.

3.1.3.c. ICT Equipment and Aerodynamic Shape Alter-

ation. Figure 2 shows a schematic view of information and

communications technology (ICT) devices that could be

added onto a generic CAV. Manufacturing ICT devices is

highly carbon-intensive,

which increases GHG emissions

associated with vehicle manufacturing. Moreover, additional

ICT devices in CAVs are expected to consume more auxiliary

power, which implies more operational energy use.

Although

highly uncertain, Gawron et al. suggested that CAV subsystems

and ICT equipment could increase a vehicle’s life-cycle

primary energy use and GHG emissions by 3−20% because

of increases in power consumption, weight, and data

transmission.

Furthermore, adding ICT devices, such as GPS antennae

and LIDAR (light detection and ranging), could alter vehicle

aerodynamics. ICT devices can create sharp edges and increase

frontal projected area, both generate turbulence around the

vehicle at high speeds and force the vehicle to consume more

energy to maintain its performance. This could dramatically

reduce CAV fuel eﬃciency at high speeds. There is no

empirical data to evaluate how signiﬁcantly add-on ICT

devices aﬀect aerodynamics and eﬃciency, but the magnitude

of impacts can be roughly approximated using eﬀects of roof

racks on conventional vehicles. Chen and Meier reported that

a roof rack can increase a passenger car’s fuel consumption by

up to 25%.

Future CAV designs could integrate ICT

equipment into the vehicle body better than the example

shown in Figure 2, potentially improving aerodynamics.

3.1.4. Platooning. Platooning is synchronized movement of

two or more vehicles trailing each other closely. Platooning

reduces aerodynamic drag for following vehicles, making the

whole platoon more eﬃcient. Aerodynamic drag forces are

proportional to the second power of speed, meaning that

platooning is most eﬀective in high speeds. Since platooning is

practically viable for highways, adoption of this technique

Figure 2. Key technologies and additional ICT devices in a generic CAV for navigation and communication. This ﬁgure is a generalized model

based on components and subsystems described in the literature.

6,73

Actual engineering designs will vary among automakers and vehicle models,

and future designs are likely to change as CAV engineering advances. Additional information about these components are provided in SI (S1).

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could yield signiﬁcant fuel savings and emissions reductions.

The magnitude of beneﬁts depends on a number of platoon-

speciﬁc characteristics, including cruising speed, speed

variations, vehicle trailing space, vehicle shape (baseline

aerodynamics), platoon size, the fraction of time spent on

the highway, and the control algorithms used by the

vehicles.

19,74

Vehicles in the middle of a platoon realize the

largest energy eﬃciency gains, while gains are smaller for

vehicles at the front and rear of a platoon. Longitudinal

controls, sensing, and V2V communications make it possible

for CAVs to safely trail each other at close distances, enabling

platooning.

Because of the relatively slow reaction time of

humans, platooning is not safe when the driver is in the loop

(i.e., when driving is not fully automated).

A number of studies have experimentally shown the energy

and emission eﬀects of drag minimization by vehicle

platooning.

58,75,76

Many of these experiments have focused

on trucks. Given the large frontal area and high percentage of

highway cruising mileages in commercial heavy-duty trucks,

truck platooning would yield substantial energy savings.

Tsugawa reported that a 3-truck platoon traveling at 80 km/h

achieves a 10% drop in energy consumption (relative to three

trucks traveling conventionally) when there is a 20-m gap

between trucks, and a 15% drop when the gap narrows to 5

For platoons containing mixed vehicle types separated by

half- to full-vehicle lengths, the drag reduction is reported

between 20 and 60%.

Wang et al. showed that a higher

penetration rate of intelligent vehicles (similar to CAVs) in a

tight platoon (i.e., a platoon with a very small gap between

vehicles) could result in lower nitrogen oxide emissions.

Barth et al. projected 10−15% energy savings for platoons

operating at separations of less than 4 m.

Similarly, Brown et

al. estimated about 20% energy savings during the approx-

imately 50% of personal vehicle travels that typically occur on

highways, equating to a 10% improvement in energy eﬃciency

overall.

Platooning in dedicated lanes results in the highest

environmental beneﬁts. However, there are still beneﬁcial

opportunities for groups of two or more CAVs to platoon on

mixed-use roads or lanes.

Platooning can also mitigate

congestion and expand roadway capacity (discussed in section

3.2.4). Although the environmental beneﬁts of platooning have

been proven, research is needed to quantify expected beneﬁts

at various CAV penetration scenarios. Realizing beneﬁts also

requires new engineering design for safe platoon maneuvers

including exiting a platoon and mergingfor various vehicle

types.

3.2. Transportation System Level. Large-scale pene-

tration of CAVs will change transportation network loads

and consequently environmental impacts associated with the

transportation system. The net result is diﬃcult to predict,

particularly for diﬀerent levels of CAV market penetration.

Major mechanisms by which CAVs aﬀect environmental

impacts of the transportation system include changing travel

cost, changing mobility services, and inﬂuencing congestion

and roadway eﬀective capacity.

3.2.1. Travel-Cost Implications. CAVs allow passengers

who would normally be driving to instead occupy travel time

with a variety of activities, such as working, reading, watching

movies, or eating. By substituting driving for productive or

leisurely activities, the perceived cost of in-vehicle time (often

called “value-of-travel time”(VOTT) or “willingness to pay”to

save travel time) could be diminished. Moreover, eliminating

the labor cost of human drivers in transportation services

reduces direct travel cost and hence expands access to

transportation services for lower-income individuals and

households. This socioeconomic beneﬁt could have accom-

panying environmental beneﬁts if transportation services

become cheap enough that lower-incomes substitute trans-

portation services for private vehicles and if transportation

services employ energy-eﬃcient CAVs, since lower-income

households tend to drive less eﬃcient vehicles.

However,

lowered travel cost is expected to increase travel demand, a key

eﬀect that could yield undesired consequences.

Many studies have attempted to analyze the general cost of

travel in CAVs. It is found that SAEVs could proﬁtably reduce

fees charged to passengers by up to 80% compared with a ride-

on-demand trip today, a drop that would make SAEVs price-

competitive with mass transit.

Chen and Kockelman

suggested that the total cost of charging infrastructure, ﬂeet

ownership, and energy for SAEVs ranges from $0.42 to $0.49

per occupied mile of travel,

which is substantially lower than

current costs of traveling in taxis or ride-hailing services.

Greenblatt and Saxena showed per-mile operation cost of high-

VMT SAEVs are about one-ﬁfth of typical per-mile taxi fares.

Lu et al. found that automated taxis (electric and conventional)

could reduce daily commute costs by over 40% but increase

total transportation-related energy consumption and emissions

in Ann Arbor, MI.

Bosch et al. provided a more conservative estimate,

indicating that shared and pooled CAV travel is likely to be

only slightly less expensive than personal vehicle travel in terms

of per-passenger-kilometer cost. This is because of the higher

capital cost and cleaning and maintenance needs of shared

ﬂeets. They also asserted that private ownership of CAVs

might be cost-competitive, despite the general assumption that

SAV-based travel is cheaper than private CAV-based travel.

Wadud analyzed the total cost of ownership for CAVs and

implications for diﬀerent levels of income. The study

concludes that full automation in personal vehicles oﬀers

substantial beneﬁts for the wealthy who have a higher value of

time and drive more frequently. In contrast, full automation in

commercial taxis is beneﬁcial to all income levels.

The upshot is that while reducing travel costs is a positive

externality likely to improve access to aﬀordable travel options,

transit equity, and consumer welfare, it may result in higher

levels of energy consumption and environmental impacts at the

transportation system level due to rebound eﬀects (discussed

further in section 3.4). This may oﬀset some eﬃciency beneﬁts

of CAVs at the vehicle level. Moreover, the lower cost of CAV

travel may discourage travelers from ride-sharing, since the cost

savings associated with SAVs over private CAVs may not be

substantial enough to be worth the extra hassle and reduced

privacy.

3.2.2. Changed Mobility Services. CAVs could reshape

mobility services by promoting shared mobility and interacting

with mass transit, as discussed below.

3.2.2.a. Shared Mobility. Large-scale penetration of CAVs

has the potential to shift the transportation system from relying

on privately owned vehicles to a new system relying primarily

on on-demand shared mobility services,

commonly known as

“Mobility as a Service”(MaaS).

Shared mobility is an

eﬀective way to reduce VMT by combining trips that are

temporally and spatially similar, generating many beneﬁts

including eﬃciency improvements, ﬂeet downsizing, conges-

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tion reduction, energy conservation, and emissions alleviation.

These beneﬁts are maximized by combining shared mobility

and vehicle automation.

CAVs can help boost car-sharing by improving user

experience, avoiding vehicle unavailability and inaccessibility.

Kang et al. proposed a system-optimization framework for

automated EV sharing and suggested higher proﬁtability and

lower emissions per passenger-mile of operation compared to

conventional car-sharing services.

CAVs can also help

improve ride-sharing eﬃciency. Ride-sharing is intended to

improve vehicle occupancy by ﬁlling empty seats in vehicles

with riders on similar routes. Compared to car-sharing, ride-

sharing is more dynamic and reliant on real-time matching.

Ride-sharing is particularly suited to CAV ﬂeets that can

continuously reroute based on real-time ride requests. Since

SAVs have not yet been tested in the real world, most studies

examining the topic have attempted to simulate the impact of

implementing a SAV ﬂeet in a speciﬁed area using agent-based

models rather than empirical data.

26,29,31,86

There are several ways in which combining shared mobility

with CAVs can reduce travel costs. First, shared mobility

systems spread ownership costs (i.e., depreciation, ﬁnancing,

insurance, registration, and taxes) and operational costs across

a large user base.

Second, the shift from personally owned

vehicles to on-demand SAVs could maximize capacity

utilization and improve vehicle utilization rate. For instance,

the average daily parking time of current private vehicles is

more than 90%, with the average daily driving of approximately

30 miles.

However, a SAV could travel more than 200 miles

and complete around 20 trips per day on average, which

translates into a more eﬃcient vehicle utilization.

26,31,87

Third,

high vehicle occupancy decreases energy use per passenger-

mile-traveled, which reduces the fuel cost for each passenger.

Finally, a transportation system that integrates SAVs can

beneﬁt from the eﬃciency of centralized planning. Decisions

made at ﬂeet management businesses are more likely to

consider fuel costs and prioritize eﬃciency compared to

individual vehicle owners, who are likely to prioritize the utility

of their vehicles.

A number of studies ﬁnd similar or lower costs for SAVs

compared to current taxi services which on average cost

approximately $0.80 to $5.75 per passenger-mile.

26,32,37,43

Fagnant and Kockelman conducted various simulations and

found that the per-mile cost of a SAV ﬂeet is around $1.00.

Chen at al. estimated that the per-mile cost of a SAEV ﬂeet

ranges from $0.75 to $1.00.

Bauer et al. reported the range of

$0.29−0.61 per revenue mile of SAEV operation as a

replacement for NYC taxis, which is an order of magnitude

lower than the cost of present-day service.

SAVs also make it possible to decrease total ﬂeet size and/or

number of vehicles operating at a given time. This yields traﬃc

and environmental beneﬁts by reducing congestion, increasing

highway capacity, and lowering emissions (further discussed in

section 3.2.3). Alonso-Mora et al. showed that introducing

high-capacity CAVs with dynamic ride-sharing could sub-

stantially downsize the NYC taxi ﬂeet. They demonstrated that

using ten-passenger-capacity CAVs could serve 98% of the

travel demand with a mean waiting time of 2.8 min, while

shrinking the taxi ﬂeet to 15% of its present size.

SAVs also

make it possible to decrease the size of the private vehicle ﬂeet

while meeting current travel demand. Studies showed that one

SAV could feasibly replace anywhere from 5 to 14 private

vehicles.

26,29,31,88,89

The replacement rate of SAEVs depends

on battery capacity and charger availability.

33,87

SAEVs have

lower replacement rates than SAVs because SAEVs need to be

charged, a process that takes longer than conventional

refueling. Hence more SAEVs than SAVs are needed to meet

the same travel demand, since there must be suﬃcient SAEVs

available to provide service while other SAEVs are charging.

3.2.2.b. Interaction with Mass Transit. Besides providing

door-to-door mobility service, CAVs could interact with other

transportation modes, such as public transit. CAVs oﬀer a

convenient option for short, frequent trips, such as traveling

from subway stops and bus stations to work or home.

Integrating CAVs with mass transit therefore provide a

promising solution to the “ﬁrst/last-mile”problem, making

mass transit more convenient which can in turn reduce

vehicular travel.

Moorthy et al. found that traveling via public

transit with CAV last-mile service could reduce energy

consumption by up to 37% compared to traveling with

personal vehicle.

If automation could be expanded to buses

and rail, labor cost savings could be passed onto passengers via

lower trip fares, thereby improving the competitiveness of mass

transit. CAV services could also be used by transit agencies in

public-private partnerships to supplement or replace costly

services such as low-ridership bus lines or paratransit.

In contrast, CAV adoption could decrease the number of

mass transit users since inexpensive CAVs could compete with

transitsystems.Similarly,low-cost,CAV-enabledshared

mobility may result in less ridership for mass transit. Less

revenue for mass transit has a disproportionate impact on low-

income population, since low-income population tends to rely

on transit more heavily than higher-income population.

Further studies are needed to quantify the likely impact of

CAVs in this regard.

3.2.3. Vehicle Utilization. In a CAV-enabled transportation

system, more people would likely be willing to travel extended

routes by car

42,91

since the burden of driving is eliminated.

Given that CAVs, unlike human drivers, do not need to rest,

their deployment is likely to increase vehicle utilization and/or

vehicle-hours-traveled. This translates to increased total VMT,

energy use, and emission (further discussed in section 3.4.1).

Some studies have also found that replacing personal

vehicles with SAVs will generate unoccupied VMT (e.g., as a

vehicle returns to its origin after dropping oﬀpassengers),

leading to higher total VMT at the transportation system level.

The extent to which total system-wide VMT will change

largely depends on how frequently trips are shared.

Fagnant

and Kockelman found that if rides are never shared, a SAV-

only ﬂeet will generate 8.7% more VMT compared to a private-

vehicle-only ﬂeet, but allowing dynamic ride-sharing in a SAV

ﬂeet reduces this ﬁgure to 4.5%.

Similarly, Zhang et al.

showed that a pooling SAV ﬂeet generates 4.7% less VMT than

a nonpooling SAV ﬂeet.

Taking realistic traﬃcﬂows into

account, Levin et al. reported that empty repositioning trips

made by SAVs without dynamic ride-sharing increase

congestion and travel time by 3−20%.

SAEVs could also

drive to remote locations for charging, resulting in higher

VMT. Loeb et al. estimated that travel to charging stations

accounts for about 32% of unoccupied VMT in SAEV ﬂeets.

Zhang et al. suggested that private CAVs can also generate

unoccupied VMT if they reduce the number of household

vehicles while maintaining the current travel patterns. For

instance, a privately owned CAV could take one member of

household to work, return home unoccupied, and then take

another member to school. This study estimated that such

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relocation could increase total VMT for privately owned

vehicles by around 30%.

It is possible that the adverse environmental eﬀects of CAV-

related VMT increase at the transportation system level could

be oﬀset by CAV-related eﬃciency gains at the vehicle level

(section 3.1).

17,42

It is important to note that most studies on

CAV utilization assume a low SAV adoption rate (around

10%).

87−89

Increasing SAV penetration is likely to save system-

wide VMT compared to a private-vehicle-only ﬂeet, since more

opportunity is available to consolidate sharable VMT and

reduce unoccupied travel of SAVs due to the reduced need of

vehicle relocation between trips. Moreover, some argue that

CAVs could help avoid unnecessary “cruising for parking”

VMT through automated navigation and parking.

Increasing

the waiting time deemed tolerable for automated taxis would

further reduce total VMT and required ﬂeet size.

26,43

3.2.4. Congestion and Road Capacity. Traﬃc congestion

and idling contribute to additional energy use and emissions.

Every new vehicle on the road uses capacity and increases

congestion. Constructing new roads and lanes is one way to

alleviate congestion. However, research has demonstrated that

induced vehicle travel (shifts from other modes, longer trips

and new vehicle trips) often consumes a signiﬁcant portion of

new capacity added to congested roads.

Alternative, arguably

more sustainable options are to encourage mixed-land use and

promote ride-sharing. Since SAVs can replace conventional

cars at a higher rate and increase vehicle utilization eﬃciency

(both leading to ﬂeet downsizing), they can reduce congestion

without adding road capacity. CAVs can expand eﬀective road

capacity by not only decreasing the number of vehicles on

road, but also right-sizing vehicles.

Vehicle right-sizing will

substantially reduce the fraction of ﬂeets composed of large

vehicles traveling frequently with few passengers.

13,37

While

the impacts of vehicle right-sizing and ﬂeet downsizing on

improving road capacity are intuitive and frequently

mentioned, quantitative estimates are missing from the

literature.

Traﬃc jams resulting from collisions can cause congestion

too. The safety improvements of CAVs is estimated to reduce

congestion by 4.5% through decreasing crash frequency.

CAV technology can also alleviate congestion and improve

eﬀective roadway capacity by allowing vehicles to safely reduce

following distance (headway), use existing lanes and

intersections more eﬃciently by maintaining shorter distances

between vehicles,

80,94

travel in coordinated platoons, take

routes that avoid traﬃc jams and low-speed zones,

and also

dampen stop-and-go traﬃc waves.

Another beneﬁt is that

CAVs can operate on a ﬂat speed range 30−70 MPH on

arterial roadways, which helps reduce traﬃc congestion.

Finally, CAV technology enables vehicles to synchronize

movement with traﬃc signals, which reduces frequent

acceleration and deceleration at intersections (also discussed

in section 3.1). Some studies have suggested that it may be

ultimately possible to achieve “signal-free”transportation

systems under high CAV penetration.

54,80

Realizing such

systems require major infrastructure overhauls as well as

technical solutions to address pedestrian movement.

Multiple studies consider the aforementioned points in their

simulations. Auld et al. applied an integrated model to analyze

the impact of diﬀerent market penetrations of CAVs on

performance of the transportation network and changes in

mobility patterns for the Chicago region. They presented a

scenario in which CAVs could yield an 80% increase in road

capacity with only 4% induced additional VMT.

Li et al.

found high-CAV-penetration scenarios can reduce carbon

monoxide, PM2.5, and energy consumption in urban areas by

up to 15% because of reduced congestion or increased road

capacity.

It is possible that vehicle automation could increase travel

demand, thereby oﬀsetting decongestion beneﬁts. Zakharenko

held that the impact of induced travel is unlikely to be very

large, since CAVs and SAVs are expected to operate far more

eﬃciently even if their utilization increases.

Additional

research is needed to estimate the expected eﬀects of increased

travel demand on road congestion and capacity at various CAV

penetration levels.

3.3. Urban System Level. Today’s urban systems have

largely been designed to accommodate privately owned and

driven cars. CAVs can reshape urban systems and infra-

structure in several ways. Because of improved communica-

tions, CAVs may require less infrastructure, such as traﬃc

lights, parking lots, and road lanes. CAVs can also resolve

charging-infrastructure challenges, thereby supporting vehicle

electriﬁcation. However, CAVs will require additional ICT

supports, though such supports could potentially be integrated

into existing street lights, signs, and other transportation

infrastructure. There are also concerns that CAVs could

encourage suburbanism and urban sprawl.

3.3.1. Infrastructure Implications. Deployment of CAVs

will revolutionize the conventional urban infrastructure. V2I

and higher safety capabilities of CAVs may render much

existing infrastructure obsolete, while requiring new types to be

installed. The net environmental impacts of CAV-related

changes in infrastructure are largely unknown. The following

sections summarize what is known and highlight priority

research areas.

3.3.1.a. Existing Infrastructure (Lighting and Traﬃc

Signals). Because CAVs may not need lighting for navigation

or signaling, it may be possible to save energy by reducing the

number or utilization of road lights and traﬃc lights. There is

no direct data on the energy demand of road lighting and

traﬃc signals in the U.S. The EIA estimates that in 2015, about

404 TWh of electricity was used for residential and commercial

lighting.

This was about 15% of the total electricity

consumed by both of these sectors and about 10% of total

U.S. electricity consumption. On the basis pf the Department

of Energy’s report on U.S. Lighting Market Characterization,

we estimate that highway lighting (excluding traﬃc signals)

consumes around 1% of electricity generated in the U.S. Thus,

reducing road lighting by 30% would save 16.5 TWh of energy,

11 MMTs of CO2eq, and around $1.65 billion annually. As a

comparison, in the UK, road lighting and traﬃc signals

consume 2.5 TWh of electricity annually, representing 0.73%

of total annual electricity consumption.

Nevertheless, navigation is not the sole purpose of road

lighting. Many passengers may not feel safe on dark roads even

if CAVs can drive without risk. Some studies proposed

replacing conventional road lights with intelligent and adaptive

systems.

98,99

These systems could turn lights on when a CAV

approaches and dim or turn lights oﬀwhen the roadway is

empty. V2I capabilities of CAVs facilitates such technology.

Future research should examine the potential for reducing road

lighting at various levels of CAV penetration from cost,

maintenance, and passenger-comfort standpoints. Research

should also consider diﬀerent technical scenarios. For instance,

the ongoing transition to light-emitting diode (LED) street

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lighting is increasing eﬃciency and so lessens the impact of

eliminating lighting altogether.

3.3.1.b. New Infrastructure Requirements. Communication

and data transmission are essential to CAV operations. CAVs

depend on high frequency of information exchange for ﬁnding

pick-up locations, eﬃcient routing, and arriving safely at the

ﬁnal destination. All this communication and data processing

requires signiﬁcant computational resources and large-scale

infrastructure (e.g., datacenters). The life-cycle of ICT

infrastructure is energy intensive and generates a variety of

environmental impacts.

71,100,101

Kolosz and Grant-Muller

considered embodied emissions of roadside infrastructure

and datacenters for the Automated Highway System (AHS), a

system that accommodates vehicles with intelligent speed

adaptation features. They reported that, despite these

emissions, AHS would save an expected 280 kilotons of

CO2eq over 15 years of operational usage in the M42 corridor,

the UK’s busiest highway. This is because AHS-enabled

optimization of vehicles on highways reduces emissions to an

extent that oﬀsets infrastructure-related emissions.

research is needed to quantify the expected net energy use and

life-cycle environmental impacts of a typical datacenter for

management and communications of CAV ﬂeets.

3.3.2. Integration of CAVs with Power Systems. As

discussed in section 3.1.2, vehicle automation and electriﬁca-

tion are mutually reinforcing. Integrating CAVs with urban

power systems can oﬀer multiple environmental beneﬁts.

102

Fleets of CAVs can help promote vehicle electriﬁcation by

resolving challenges such as range anxiety, access to charging

infrastructure, and charging time management, since con-

nected vehicles are always aware of the availability and location

of charging options.

33,46

Automated charging infrastructure enables more eﬃcient

energy management and facilitates vehicle-grid integration and

uptake of renewable electricity in transportation sector. Some

prototypes of charging robotic arms and mechanisms have

recently been introduced to automatically plug into EVs and

control the charging process. Wireless power transfer (WPT) is

a nascent technology that can complement CAVs.

103

When

wireless charging is combined with CAVs, it becomes possible

to automatically rotate vehicles on charge transmitter pads

without human intervention. Removing this labor cost for

service would make SAEVs cheaper. In addition, CAVs could

navigate themselves to wireless charging spots to top up at

reduced energy rates during oﬀ-peak hours. Chen et al.

investigated the charging-infrastructure requirements of SAEVs

and concluded that by replacing attendant-serviced charging

with automated wireless charging, the operational cost of

SAEV ﬂeets drops by 20−35%.

A step beyond stationary WPT is in-motion dynamic

charging, in which embedded transmitters in roadways

wirelessly charge vehicles as they are moving, extending

maximum range or reducing the required size and cost of

batteries.

103

Lavrenz and Gkritza studied the automated

electric highway systems (AEHS) powered by inductive

charging loops embedded in the roadway and estimated that

AEHS would decrease fossil-fuel energy use by more than 25%

and emissions by up to 27%.

An interesting potential use of electric CAVs is as mobile

energy storage units for excess electricity generated by utility-

scale power plants. Under such a scheme, CAVs would

automatically charge (take up power) at oﬀ-peak hours when

rates and demand are low and discharge (release power) back

to the grid during peak hours or in case of an electricity

storage. Such bidirectional power transfer could be managed

by CAV communications with the power grid and would be

particularly useful in facilitating increased penetration of

intermittent renewable energy like wind and solar. One caveat

is that frequent charging and discharging of vehicle batteries

might result in accelerated battery degradation.

103

Another is

that some consumers might be reluctant to allow their privately

owned vehicles to be leveraged in such a manner, even if

ﬁnancial incentives were provided.

104

It is also important to note that the charging patterns of

SAEVs and privately owned CAVs might be very diﬀerent from

charging patterns of human-driven EVs including privately

owned EVs as well as EVs owned by transportation network

companies.

23,43

SAEVs might need more frequent charging

given their higher utilization rate (discussed in section 3.2.2).

The impacts of diﬀerent charging patterns on the grid and

associated environmental consequences are uncertain and

require further investigation.

3.3.3. Land Use. Because CAVs can navigate themselves to

and from dedicated parking areas, increased CAV penetration

reduces the need for parking located close to all destinations

and hence the total amount of space needed for parking

overall.

Nourinejad et al. noted that CAVs can park in much

tighter spaces, reducing needed parking space by what they

found to be an average of 67%.

105

Similarly, Zhang and

Guhathakurta suggested that SAVs could reduce parking land

by 4.5% in Atlanta at penetration as low as 5%.

Avoiding the

construction of new parking could also have substantial

environmental beneﬁts. Chester et al. reported that parking

construction can add 6−23 g CO2eq per passenger-kilometer-

traveled to the total life-cycle emissions of a vehicle (typically

about 230 to 380 g CO2eq) and increase sulfur dioxide and

PM10 emissions by 24−89%.

106

Eliminating obsolete transportation infrastructure could

enable denser development in urban areas.

However, there

are concerns that CAVs could encourage suburbanism and

urban sprawl, especially for people with lower perceived values

of travel time. According to Bansal et al., deployment of CAVs

will likely result in long-term shifts in which people choose to

relocate their homes.

Large families or those who tend to

take advantage of lower land prices in suburbs may use CAVs

to reside further from urban cores.

107

Zakharenko provided a

comprehensive overview of how urban areas could be altered

by CAV deployment.

Such qualitative discussion is common

in the literature, but more quantitative analyses are needed to

inform land-use policies and urban planning.

3.4. Society Level. The potential environmental implica-

tions of vehicle automation are the largest at the society level,

but the magnitude and direction of inﬂuences are highly

uncertain. One key factor is the eﬀect that CAVs will have on

public perception of mobility. For many decades, cars have

been used to make a statement about individual personalities

and values and often to ﬂaunt wealth. Moreover, automakers

are strongly motivated to maintain the current emotional

connection of consumers to their cars,

unless they adopt new

business models. Public perception of shared and automated

driving versus private, human driving will aﬀect the extent to

which people are willing to give up private vehicles in favor of

CAVs, how car manufacturers develop and market CAVs, tax

and insurance policies, and infrastructure investments. Given

that CAVs are not yet commercially available, assessing public

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opinion and consumer choice on market penetration is

challenging.

39,74

A number of surveys and questionnaires have quantiﬁed

early public perception of various CAV technologies. Bansal et

al. surveyed Texan families and found that more than 80% of

respondents would increase vehicle utilization under a CAV

paradigm.

107

König and Neumayr provided empirical evidence

on mental barriers and resistance toward CAVs and suggested

that people are ready and interested in riding with CAVs but

not willing to buy one.

Kyriakidis et al. surveyed 5000 people

on their acceptance of, concerns about, and willingness to buy

partially, highly, and fully automated vehicles. Results indicate

that respondents who are willing to pay more for fully

automated vehicles are likely to have higher annual VMT and

utilization rates.

Wadud et al.

and Anderson et al.

stated

that the utilization of privately owned CAVs and induced travel

demand are expected to have game-changing inﬂuence on their

energy consumption and environmental impacts.

A signiﬁcant negative externality of CAVs will be reduction

in demand for human labor in services such as taxis, trucking,

and delivery, thus potentially unemployment for many service

drivers. But CAVs are expected to generate new and high-

quality jobs in hardware/software technologies and in ﬂeet

management and services.

3.4.1. Behavioral Response and Travel Pattern Shift. The

convenience, accessibility, and lower travel cost of CAVs may

shift travel patterns and induce higher travel demand, mainly

due to travel behavior changes. As discussed in section 3.2.1,

automated driving would allow people to participate in other

pursuits during their trips, lowering the perceived cost of travel

and increasing acceptable commute distance and time.

17,38,42

People may prefer SAVs and SAEVs to public transit if costs

are comparable, since the former options provide door-to-door

service. Similarly, for short trips, people may substitute CAVs

for otheroften more sustainable and activemodes such as

walking or cycling. It is also possible that travelers consider

rechaining their trip needs (shopping, recreational, commute,

errands, etc.) once they have access to CAV technology.

Overall, CAVs have the potential to replace not only private

vehicles but many other types of transportation.

CAVs could also unlock additional travel demand from

people who have unmet travel needs and previously cannot or

choose not to drive (e.g., the elderly, the young, unlicensed

individuals, and people with driving-restrictive medical

conditions or disabilities). CAVs can provide door-to-door

mobility service for these populations that is cheaper and more

convenient than current options like paratransit or taxis.

Expanded mobility for currently underserved population is

highly desired from an equity and ethical standpoint but is

likely to increase trip frequencyespecially in suburban,

vehicle-dependent areas.

Harper et al. estimated that the

increase in travel demand from travel-restricted population

could be as much as an additional 14% VMT (equivalent to

295 billion miles) per year in the U.S.

Increased travel demand associated with CAVs represents a

type of “rebound eﬀect.”In the energy economics, rebound

eﬀects describe the percentage of energy savings from a new,

energy-eﬃcient technology that are oﬀset by increased use of

that technology.

108

Similarly, eﬃciency gains from CAV

technology at the vehicle level may induce additional travel

demand and consequently oﬀset environmental beneﬁts at the

society level. Such rebound eﬀects can cause discrepancies

between predicted and realized net impacts of CAVs and other

transportation innovations.

109

For CAVs, the rebound eﬀect is one of the mechanisms

connecting diﬀerent system levels. Milakis et al. presented a

ripple model to conceptualize rebound eﬀects in societal

aspects of automated driving.

Wadud et al. used a simple

approach to employ rebound eﬀects from generalized cost of

travel as a multiplier of CAV travel activity by simulating a

range of literature-driven travel elasticities.

In short, it is

widely accepted that rebound eﬀects could oﬀset environ-

mental beneﬁts of CAVs, but there is signiﬁcant uncertainty

about the extent. Considering the importance of this issue for

the environment as well as for transportation and infrastructure

planning, additional eﬀort to model and quantify CAV-related

rebound eﬀects is urgently needed.

3.4.2. Shared Consumption. Public opinion on private

vehicle use and social norms over vehicle ownership may

change along with the introduction of shared mobility in the

transportation sector.

32,110

CAVs can help change public

perception of shared consumption by facilitating and

promoting shared mobility.

111

The millennial generation has

already shown diﬀerent transportation preferences and

opinions compared to prior generations.

107,110,111

We spec-

ulate that this shift might be extended to other types of goods

and services. In a society where shared consumption is

mainstream, desire for product ownership will be reduced,

which will reduce environmental impacts associated with

product life-cycles. CAV-facilitated shared mobility can

support this change from a technological perspective, but

questions remain as to adoption behaviors and public

acceptance. The literature does not yet show what future

travelers will want from their transportation systems.

3.4.3. Transformation of Other Sectors. Widespread

deployment of CAVs may also inﬂuence other transportation

industries, such as aviation and rail. Given the lower cost of

CAV travel, certain groups of users may choose to take longer

trips using road transportation rather than aviation or rail. This

is environmentally signiﬁcant, as aviation and rail tend to have

lower marginal energy use and emissions on a per-passenger-

mile-traveled basis compared to low- or single-occupancy

vehicles.

Both intercity rail (56.1 passenger-miles per

gasoline-gallon equivalent (GGE)) and airlines (50.0 pas-

senger-miles per GGE) have higher energy eﬃciency compared

to passenger vehicles (38.9 passenger-miles per GGE).

112

LaMondia et al. studied the impact of CAVs on long-distance

travel choices by analyzing travel surveys, and concluded that

CAVs could displace 25−35% of demand for air travel for trips

of 500 miles or more.

113

The environmental impact of this shift

could be mitigated if intercity CAV travels were mostly

through larger shared vehicles such as autonomous buses.

CAVs are also likely to aﬀect a variety of transport-intensive

sectors and services. For instance, CAVs could serve as mobile

overnight sleeping compartments, decreasing demand for

hotels for long-distance trips.

Sectors that heavily utilize

freight transportationonline retail, the food industry,

etc.will likely beneﬁt from the emergence of CAVs. The

environmental impacts of CAV adoption and utilization in

these sectors are likely signiﬁcant, but little is known.

research is needed to measure these broader impacts and

inform relevant policymaking.

3.4.4. Workforce Impacts. Vehicle automation will render

many jobs obsolete, speciﬁcally in labor-intensive trans-

portation services such as freight trucking, public transit, and

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taxi driving.

27,42

The U.S. Department of Commerce estimates

that 15.5 million U.S. workers are employed in occupations

that could be aﬀected by the introduction of automated

vehicles.

114

Unemployment has attendant economic and social

consequences. These include altered consumption patterns

(usually moving toward less sustainable commodities and

services), as well as adverse physical and mental health

eﬀects.

Both these consequences have environmental

relevance as consumption pattern changes drive changes in

supply chain and associated environmental impacts. It should

be noted that CAV-related job losses will occur gradually in

most cases. For instance, early automated trucks will still

require human drivers to assist with loading and unloading,

navigation, fueling, and maintenance. Over time, though,

retraining the workforce and alternative job opportunities will

be needed to ensure sustainable CAV adoption and mitigate

adverse outcomes.

One option is to help workers in

transportation-related jobs transition to sectors that are likely

to expand as CAV penetration grows. These sectors include

but not limited to hardware and software development, ﬂeet

management, and concierge services.

3.5. Summary of Environmental Impacts of CAVs. Our

review shows that due to the complexity and interdependence

of higher levels of interactions, the uncertainty of CAV-related

environmental impacts increases as the impact scope broadens.

Most studies related to energy and environmental impacts of

CAVs have tried to identify eﬀect bounds and speculate on

system-level impacts. Collectively, these studies conﬁrm that

CAV technology has the potential to deliver large environ-

mental beneﬁts, but realizing this potential highly depends on

deployment strategies and consumer behavior. The greatest

energy and environmental impacts will not stem from CAV

technology directly, but from CAV-facilitated transformations

at all system levels.

At the vehicle level, CAV technology can signiﬁcantly

enhance eﬃciency. Considerable fuel savings and emission

reduction can be achieved through CAV design oriented

toward energy eﬃciency. Studies reviewed in this paper report

vehicle-level fuel savings ranging between 2% and 25% and

occasionally as high as 40%. Integrating CAV technology and

vehicle electriﬁcation can considerably improve the economics

and attractiveness of transportation decarbonization. Higher

CAV penetration could further alleviate negative environ-

mental impacts of road transportation through large-scale,

connected eco-driving. However, the net eﬀect of CAV

technology on energy consumption and emissions in the

long term remains uncertain and depends on other levels of

interactions with the environment.

At the transportation system level, CAV-related environ-

mental beneﬁts derive from optimization of ﬂeet operations,

improved traﬃc behavior, more eﬃcient vehicle utilization, and

the provision of shared mobility services. Speciﬁcally, shared

mobility and CAV technology have signiﬁcant mutual

reinforcing eﬀects.

At the urban system level, CAVs could reshape cities by

changing land-use patterns and transportation infrastructure

needs. For instance, street lighting and traﬃc signals could

become less necessary or obsolete under a CAV paradigm,

resulting in energy savings. However, CAVs could encourage

urban sprawl and shifting to peripheral zones with longer

commutes. CAVs also require communications with large-scale

datacenters, which are generally energy intensive. At the same

time, CAVs can facilitate integration of EVs and charging

infrastructure into power grids. These urban-level mechanisms

might not deliver signiﬁcant net environmental beneﬁts

without high penetration of CAV technology.

While long-term net environmental impacts of CAVs at the

vehicle, transportation system, and urban system levels seem

promisingly positive, the lower cost of travel and induced

demand at the society level is likely to encourage greater

vehicle utilization and VMT. Most studies reviewed in this

paper assume current travel patterns, vehicle ownership

models, and vehicle utilization without considering realistic

behavioral changes resulted from increased CAV penetration.

Society-level impacts of CAVs will undoubtedly be profound,

but signiﬁcant uncertainties exist about behavioral changes,

making it very diﬃcult to project the actual energy and

environmental impacts.

The synergetic eﬀects of vehicle automation, electriﬁcation,

right-sizing, and shared mobility are likely to be more

signiﬁcant than any one isolated mechanism. Hence, these

synergies should be the focus of future research eﬀorts. Fulton

et al. projected that the combination of these technologies

could cut global energy use by more than 70% and reduce CO2

emissions from urban passengers by more than 80% by 2050.

They further estimated that the combination of these

technologies could reduce costs of vehicles, infrastructure,

and operations in the transportation sector by more than 40%,

achieving savings approaching $5 trillion annually compared to

the business-as-usual case.

To ensure truly sustainable uptake and adoption of CAV

technology, transportation systems must be more energy

eﬃcient, facilitate emissions reduction, mitigate local air

pollution, and address public health concerns. At the same

time, strategic development and deployment of CAV

technology are necessary to control overall travel demand

and congestion.

4. PRIORITY RESEARCH NEEDS

On the basis of our review of the literature, we recommend the

following four principles for improving research on the energy,

environmental, and sustainability implications of CAVs:

I. Where possible, transition to empirical, data-based

analysis of CAV impacts and revisit assumptions. The

novelty of CAV technology and lack of data means that

analysis of CAV impacts has, to date, been largely

speculative and qualitative. Moreover, many analyses are

based on oversimpliﬁed or unrealistic assumptions.

Researchers should strive to increase the rigor of CAV

studies as more data and higher ﬁdelity models become

available.

II. Improve models by more accurately characterizing

CAV impacts and better capturing uncertainty. Most

analyses have assumed the mechanisms by which CAVs

impact the environment are independent of one another.

This assumption frequently leads to underestimation or

overestimation of aggregate impacts. Furthermore,

models should better reﬂect the true nature of CAV

impacts. For instance, many studies fail to distinguish

between general trends of energy eﬃciency improve-

ment in vehicles and additional beneﬁts that are solely

enabled by CAV attributes. It is also necessary to

quantify the upper and lower bounds of impacts and

incorporate these bounds into models to better capture

and characterize uncertainty.

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III. Place more eﬀort on understanding the eﬀects of

diﬀerent CAV technologies and market scenarios on

consumer behavior and travel patterns. Although

improvements in CAV eﬃciency at the vehicle level

should not be overlooked, the largest environmental

impacts are likely to depend on consumer behavior and

travel patterns: that is, when, where, how often, and how

much consumers travel with CAVs.

IV. Integrate analysis and modeling across diﬀerent

system levels. There is a need for deeper investigation

on how mechanisms at each level reinforce or under-

mine each other. Figure 3 illustrates interactions and

linkages across the four system levels identiﬁed in this

review that are likely to have substantial energy,

environmental, and sustainability implications. The

trade-oﬀs between interactions and linkages are largely

unexplored and merit further research.

We also recommend prioritizing research on four speciﬁc

topics: CAV design and testing, development of CAV-speciﬁc

models and tools, investigation of behavioral phenomena

associated with CAV sharing and adoption, and assessment of

policy needs and opportunities. Each of these is discussed in

further detail below.

4.1. CAV Design and Testing. The evolution of vehicle

design is a major source of uncertainty for the environmental

performance of CAVs. There is a gap in the literature regarding

which factors should drive the vehicle design optimization and

decision-making protocols that will aﬀect CAV-related energy

consumption and emissions. Conventional life-cycle assess-

ment (LCA) can be used to characterize the ﬁrst-order impacts

of various design protocols and provide insights that can

improve sustainability of early CAV designs. However, for

more radical and complex designs (including vehicle right-

sizing and safety-enabled light-weighting), more sophisticated

sustainability assessments are needed. Studies should be

conducted to characterize environmental beneﬁts of diﬀerent

CAV designs under diﬀerent real-world scenarios and

particularly under diﬀerent levels of societal CAV acceptance.

Another priority should be quantifying energy eﬃciency

improvements actually achieved by early commercial designs.

Proving grounds and test facilities are needed to demonstrate

that theoretical CAV eﬃciencies can be practically achieved.

Providing researchers with real-world data from on-board

diagnostics (of current prototypes) can help identify best

practices and designs. Results can then be used to improve

real-world development and deployment.

Considerations need to be given in carrying out such

research to avoid infringing on consumer privacy or

compromise intellectual property.

4.2. CAV-Speciﬁc Models and Tools. CAVs will have

impacts on and be aﬀected by land use, demand, demographic

changes, economic factors, fueling infrastructure, and local

policies, among other factors. CAV-related changes in demand

for and supply of mobility services will change loads placed on

transportation networks. For instance, CAVs could improve

freeway traﬃcﬂows by enabling shorter following distances

between vehicles but deteriorate road congestion and eﬀective

capacity by inducing more travel. Also, current vehicle-choice

models are ill-suited to incorporate numerous consumer

preference variables relevant to CAV adoption. Moreover,

CAVs are not yet integrated into major transportation and

energy modelssuch as those used by the U.S. DOT, EPA,

EIA, and the Intergovernmental Panel on Climate Change

for estimating future travel demand, energy use, and environ-

mental consequences. In most existing assessment studies,

various measures that can reduce demand for travel or vehicle

usage and improve driving performance have been identiﬁed.

However, CAVs most likely entail considerable yet uncertain

rebound eﬀects, making current predictions of future trans-

portation demand unreliable.

Integrated assessment models

and research support tools that incorporate environmental

eﬀects of system-level CAV attributes for various market

Figure 3. Interactions and linkages between system levels that entail energy, environmental, and sustainability impacts. The linkages are illustrative

and not necessarily exhaustive.

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penetrations should be developed to enable higher-quality

projections of future travel trends.

4.3. Behavioral Studies. Scant eﬀort has been dedicated

to analyzing how consumer preference for CAV technology,

vehicle ownership, and ride-sharing might evolve. This is

important given that the net environmental impacts of CAVs

are highly dependent on the degree to which CAVs are shared

versus privately owned. Pooling and shared mobility services

alleviate most adverse environmental eﬀects of CAV

technology. However, social norms may lead people to avoid

sharing transportation with strangers, especially if cost

diﬀerences are marginal. Research is needed to identify the

factors that will aﬀect these choices. There is a particular need

to examine mixed private/shared CAV scenarios, since most

studies conducted to date examine scenarios in which CAVs

are either fully private or fully shared.

Further investigation is also needed into how readily

consumers will adopt CAVs. Real-world data can be obtained

from surveys and tests. However, surveys are probably less

useful due to the novelty of CAV technology, since most

respondents will not be able to provide an informed response.

Novel approaches are needed to investigate if and under what

circumstances people will accept CAVs and how they will use

them. Creative techniques such as virtual and augmented

reality might be useful in this regard. More extensive

engagementi.e., participants work with researchers to

understand possible technology options and more deeply

explore scenarioscould also provide deeper insight into how

people actually perceive CAV technology.

4.4. Policy Needs and Opportunities. Governments are

already playing an active role in supporting technological

development of CAVs. Emphasis has been placed on safety,

equity, and mobility, while scant attention has been paid to

environmental implications. For example, a bipartisan group of

U.S. senators recently released a set of principles for self-

driving vehicle legislation as part of the American Vision for

Safer Transportation through Advancement of Revolutionary

Technologies (AV START) Act. These principles do not

mention energy, eﬃciency, or emissions at all.

115

This

omission is problematic, given large environmental oppor-

tunitiesand risksassociated with CAV technology.

Historically, the majority of environmental policies for the

transportation sector have focused on regulating tailpipe

emissions. Since CAVs are likely to be more eﬃcient and

generate lower levels of emissions than conventional vehicles,

limiting emissions on a per-vehicle basis is less important than

considering potential environmental impacts of CAVs on a

broader scale. CAVs may induce travel demand that oﬀsets

or even eliminatesimprovements in per-vehicle eﬃciency

and emissions. It is important to develop policies that address

this concern. CAVs also provide new opportunities for

governance. Vehicle connectivity enables environmental

policies, such as mileage charges, regulation of unoccupied

travel, and dynamic emission reporting.

116

Such policies have

advantages. For instance, VMT taxation is seen as less

regressivehence more equitableand more economically

eﬃcient than fuel taxes.

117

However, collecting accurate spatial

and time-of-day vehicle use may raise privacy concerns and is

politically diﬃcult to implement.

In addition to exploring CAV-speciﬁcpolicyoptions,

policymakers should consider establishing CAV policy frame-

works that can be adapted based on how the market and

technology evolves. Several possible use cases of CAVs that

would have signiﬁcant external costs are not discouraged by

current policy, and the most beneﬁcial use cases are not

incentivized. For example, large, personally owned, ineﬃcient

CAVs could serve the owner at signiﬁcant cost to the system

by driving “selﬁshly”(for instance cruising streets empty

instead of paying for parking), and underpaying for impacts on

infrastructure. It remains to be seen whether this use case will

manifest in reality. But implementing mechanismssuch as

dynamically pricing CAV use on a per-mile basis in congested

areas or at peak timesfor addressing undesired outcomes will

be far easier now than once CAVs are already on the road.

Overall, robust understanding of energy, environmental, and

sustainability impacts of CAV technology depends on the

evolution of technology, behavioral responses, market

penetration, and regulatory and policy considerations.

Inclusion of all relevant factors to maximize environmental

beneﬁts and minimize adverse consequences is critical for the

development of this transformational transportation technol-

ogy that does not only saves lives but also improves the

environment.

■ASSOCIATED CONTENT

SSupporting Information

The Supporting Information is available free of charge on the

ACS Publications website at DOI: 10.1021/acs.est.8b00127.

Short description of CAV components (PDF)

■AUTHOR INFORMATION

Corresponding Author

*E-mail: mingxu@umich.edu.

ORCID

Hannah R. Saﬀord: 0000-0001-9283-2602

Ming Xu: 0000-0002-7106-8390

Notes

The authors declare no competing ﬁnancial interest.

■ACKNOWLEDGMENTS

Authors thank several participants of the 2017 and 2018

Automated Vehicle Symposium (Energy and Environmental

Implications of CAVs Breakout Sessions), as well as many

other experts for providing helpful suggestions, insight, and

feedback. The contribution of Dave Brenner for creating

ﬁgures is appreciated. We also thank the anonymous reviewers,

whose constructive comments substantially improved the

paper.

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Jun 2024
PROG MATER SCI

Carbon fibers (CFs) have received remarkable attention in recent decades because of their excellent mechanical properties, low density, and outstanding chemical/thermal stability. However, due to their high cost, the usage of CFs is still limited to high-end applications. Tremendous efforts have been made to fabricate cost-effective CFs by exploring alternative precursors, developing spinning methods, and optimizing processing conditions. Nevertheless, selecting a successful precursor with a matching experimental procedure is still challenging. As an alternative to the experiment, we can utilize predictive modeling at multiscale levels to understand and predict CFs’ behaviors and properties with desired accuracy yet at a significantly reduced cost. The modeling efforts can subsequently be integrated with experimental studies. This review aims to provide a comprehensive and critical overview of efforts to reduce the overall cost of CF preparation via various precursors and by including computational prediction. First, it briefly describes the progress and challenges of CFs, followed by investigating different precursors that may affect their properties. Then, state-of-the-art developments regarding experimental and computational studies for achieving low-cost CFs are discussed in detail. In the end, a summary of the current achievements and a future vision of challenges and possible solutions to obtain cost effective CFs are given.

A Systematic Assessment of the Challenges Towards Sustainable Urban Transportation: A Case Study of the Island of Mauritius

Conference Paper

Dec 2023

Influence of Surface Modification of Layered Cathode Materials LiNixMnyCozO2 by NASICON Structure Nanoparticles on Their Electrochemical Characteristics

Article

Mar 2024
Theor Exp Chem

Long-term U.S transportation electricity use considering the effect of autonomous-vehicles: Estimates & policy observations

Article

Full-text available

Nov 2018
ENERG POLICY

In this paper, we model three layers of transportation disruption-first electrification, then autonomy, and finally sharing and pooling-in order to project transportation electricity demand and greenhouse gas emissions in the United States to 2050. Using an expanded kaya identity framework, we model vehicle stock, energy intensity, and vehicle miles traveled, progressively considering the effects of each of these three disruptions. We find that electricity use from light duty vehicle transport will likely be in the 570-1140 TWh range, 13-26%, respectively, of total electricity demand in 2050. Depending on the pace at which the electric sector decarbonizes , this increase in electric demand could correspond to a decrease in LDV greenhouse gas emissions of up to 80%. In the near term, rapid and complete transport electrification with a carbon-free grid should remain the cornerstones of transport decarbonization policy. However, long-term policy should also aim to mitigate autonomous vehicles' potential to increase driving mileage, urban and suburban sprawl, and traffic congestion while incentivizing potential energy efficiency improvements through both better system management and the lightweighting of an accident-free vehicle fleet.

Multiagent Spatial Simulation of Autonomous Taxis for Urban Commute: Travel Economics and Environmental Impacts

Article

Full-text available

Dec 2018

With the likelihood of autonomous vehicle technologies in public transport and taxi systems increasing, their impact on commuting in real-world road networks is insufficiently studied. In this study, an agent-based model is developed to simulate how commuters travel by autonomous taxis (aTaxis) in real-world road networks. The model evaluates the travel costs and environmental implications of substituting conventional personal vehicle travel with aTaxi travel. The proposed model is applied to the city of Ann Arbor, Michigan, to demonstrate the effectiveness of aTaxis. The results indicate that to meet daily commute demand with wait times less than 3 min, the optimized autonomous taxi fleet size is only 20% of the conventional solo-commuting personal car fleet. Commuting cost decreases by 38%, and daily vehicle utilization increases from 14 to 92 min When using internal combustion engine aTaxis, energy consumption, greenhouse gas (GHG) emissions, and SO2 emissions are respectively 16, 25, and 10% higher than conventional solo commuting, mainly because of unoccupied repositioning between trips. Given the emission intensity of the local electricity grid, the environmental impacts of electric aTaxis do not show significant improvement over conventional vehicles.

Roads in transition: Integrated modeling of a manufacturer-traveler-infrastructure system in a mixed autonomous/human driving environment

Article

Full-text available

May 2018

This paper develops an integrated model to characterize the market penetration of autonomous vehicles (AVs) in urban transportation networks. The model explicitly accounts for the interplay among the AV manufacturer, travelers with heterogeneous values of travel time (VOTT), and road infrastructure capacity improvement due to automated driving. The model is structured as a leader (AV manufacturer)-follower (traveler) game and formulated as a mathematical program with complementarity constraints (MPCC), which is challenging to solve. We propose a solution approach based on piecewise linearization of the MPCC as a mixed-integer linear program (MILP) and solving the MILP to global optimality.

The Impact of Private Autonomous Vehicles on Vehicle Ownership and Unoccupied VMT

Article

Full-text available

Mar 2018

With 36 ventures testing autonomous vehicles (AVs) in the State of California, commercial deployment of this disruptive technology is almost around the corner (California, 2017). Different business models of AVs, including Shared AVs (SAVs) and Private AVs (PAVs), will lead to significantly different changes in regional vehicle inventory and Vehicle Miles Travelled (VMT). Most prior studies have already explored the impact of SAVs on vehicle ownership and VMT generation. Limited understanding has been gained regarding vehicle ownership reduction and unoccupied VMT generation potentials in the era of PAVs. Motivated by such research gap, this study develops models to examine how much vehicle ownership reduction can be achieved once private conventional vehicles are replaced by AVs and the spatial distribution of unoccupied VMT accompanied with the vehicle reduction. The models are implemented using travel survey and synthesized trip profile from Atlanta Metropolitan Area. The results show that more than 18% of the households can reduce vehicles, while maintaining the current travel patterns. This can be translated into a 9.5% reduction in private vehicles in the study region. Meanwhile, 29.8 unoccupied VMT will be induced per day per reduced vehicles. A majority of the unoccupied VMT will be loaded on interstate highways and expressways and the largest percentage inflation in VMT will occur on minor local roads. The results can provide implications for evolving trends in household vehicles uses and the location of dedicated AV lanes in the PAV dominated future.

The implications of the sharing economy for transport

Article

Full-text available

Mar 2018

The sharing economy has gained a lot of attention in recent years. Despite the substantial growth in shared services, its impact overall on transport is unclear. This paper analyses the literature on sharing in transport and includes government and consultant reports, websites and academic journals. The drivers of ride-sharing, car-sharing, car-pooling and freight-sharing are largely economic and convenience related for participants. Trust, technology platforms and the trend to avoid ownership of assets are facilitating factors in its growth. Over-regulation, inconsistent quality of service and the need for recommendation are potential barriers. The transport journals in particular are relatively slow to research this topic with more focusing on bike-sharing than other modes of vehicle sharing. The paper discusses the impact of sharing on transport suggesting it is likely to be part of a solution to transport problems and congestion perhaps in combination with other developments such as driverless vehicles. It also warns of the dangers of over-regulation and under-regulation. The future will require holistic transport strategies that consider sharing options and will require government departments to work cooperatively.

Autonomous or driver-less vehicles: Implementation strategies and operational concerns

Article

Full-text available

Dec 2017

Autonomous vehicles are expected to shift not only the driving paradigms but also the notion of vehicle ownership. Although autonomous vehicles are believed to introduce many safety, mobility, and environmental benefits, they will be initially priced relatively highly. This paper assesses the potential for circumventing this barrier by promoting a shared ownership program in which households form clusters that share the ownership and ridership of a set of autonomous vehicles. Such a program will increase the utilization rate of vehicles, making ownership of autonomous vehicles more economical. We study parameters that affect the benefits expected from autonomous vehicles, and introduce policy directions that can boost these benefits.

A Decentralized Energy-Optimal Control Framework for Connected Automated Vehicles at Signal-Free Intersections

Article

Jul 2018
AUTOMATICA

We address the problem of optimally controlling connected and automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling, so as to minimize energy consumption subject to a throughput maximization requirement. We show that the solution of the throughput maximization problem depends only on the hard safety constraints imposed on CAVs and its structure enables a decentralized optimal control problem formulation for energy minimization. We present a complete analytical solution of these decentralized problems and derive conditions under which feasible solutions satisfying all safety constraints always exist. The effectiveness of the proposed solution is illustrated through simulation which shows substantial dual benefits of the proposed decentralized framework by allowing CAVs to conserve momentum and fuel while also improving travel time.

Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions

Article

Apr 2018
TRANSPORT RES C-EMER

Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet. This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.

Cost, Energy, and Environmental Impact of Automated Electric Taxi Fleets in Manhattan

Article

Mar 2018

Shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50–90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05–$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.

Life Cycle Assessment of Connected and Automated Vehicles: Sensing and Computing Subsystem and Vehicle Level Effects

Article

Feb 2018

Although recent studies of connected and automated vehicles (CAVs) have begun to explore the potential energy and greenhouse gas (GHG) emission impacts from an operational perspective, little is known about how the full life cycle of the vehicle will be impacted. We report the results of a life cycle assessment (LCA) of Level 4 CAV sensing and computing subsystems integrated into internal combustion engine vehicle (ICEV) and battery electric vehicle (BEV) platforms. The results indicate that CAV subsystems could increase vehicle primary energy use and GHG emissions by 3–20% due to increases in power consumption, weight, drag, and data transmission. However, when potential operational effects of CAVs are included (e.g., eco-driving, platooning, and intersection connectivity), the net result is up to a 9% reduction in energy and GHG emissions in the base case. Overall, this study highlights opportunities where CAVs can improve net energy and environmental performance.

A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles

Abstract and Figures

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